Showing papers by "Hong Kong Baptist University published in 2020"

Plant abiotic stress response and nutrient use efficiency.

Abstract: Abiotic stresses and soil nutrient limitations are major environmental conditions that reduce plant growth, productivity and quality. Plants have evolved mechanisms to perceive these environmental challenges, transmit the stress signals within cells as well as between cells and tissues, and make appropriate adjustments in their growth and development in order to survive and reproduce. In recent years, significant progress has been made on many fronts of the stress signaling research, particularly in understanding the downstream signaling events that culminate at the activation of stress- and nutrient limitation-responsive genes, cellular ion homeostasis, and growth adjustment. However, the revelation of the early events of stress signaling, particularly the identification of primary stress sensors, still lags behind. In this review, we summarize recent work on the genetic and molecular mechanisms of plant abiotic stress and nutrient limitation sensing and signaling and discuss new directions for future studies.

Rapid Increases in Warm-Season Surface Ozone and Resulting Health Impact in China since 2013

Abstract: China’s nationwide ozone monitoring network initiated in 2013 has observed severe surface ozone pollution. This network, combined with the recent Tropospheric Ozone Assessment Report (TOAR) data set, offers a more comprehensive view on global surface ozone distribution and trends. Here, we report quantitative estimates of the warm-season (April–September) surface ozone trends and resulting health impacts at Chinese cities in 2013–2019. Both the parametric and nonparametric linear trends for 12 ozone metrics relevant to human health and vegetation exposure are derived. We find that all ozone metrics averaged from Chinese urban sites have increased significantly since 2013. The warm-season daily maximum 8-h average (MDA8) ozone levels increased by 2.4 ppb (5.0%) year–¹, with over 90% of the sites showing positive trends and 30% with trends larger than 3.0 ppb year–¹. These rates are among the fastest trends, even faster in some Chinese cities, compared with the urban ozone trends in any other region worldwide reported in TOAR. Ozone metrics reflecting the cumulative exposure effect on human health and vegetation such as SOMO35 and AOT40 have increased at even faster rates (>10% year–¹). We estimate that the total premature respiratory mortalities attributable to ambient MDA8 ozone exposure in 69 Chinese cities are 64,370 in 2019, which has increased by 60% compared to 2013 levels and requires urgent attention.

Artemisinin compounds sensitize cancer cells to ferroptosis by regulating iron homeostasis

Abstract: The antimalarial drug artemisinin and its derivatives have been explored as potential anticancer agents, but their underlying mechanisms are controversial. In this study, we found that artemisinin compounds can sensitize cancer cells to ferroptosis, a new form of programmed cell death driven by iron-dependent lipid peroxidation. Mechanistically, dihydroartemisinin (DAT) can induce lysosomal degradation of ferritin in an autophagy-independent manner, increasing the cellular free iron level and causing cells to become more sensitive to ferroptosis. Further, by associating with cellular free iron and thus stimulating the binding of iron-regulatory proteins (IRPs) with mRNA molecules containing iron-responsive element (IRE) sequences, DAT impinges on IRP/IRE-controlled iron homeostasis to further increase cellular free iron. Importantly, in both in vitro and a mouse xenograft model in which ferroptosis was triggered in cancer cells by the inducible knockout of GPX4, we found that DAT can augment GPX4 inhibition-induced ferroptosis in a cohort of cancer cells that are otherwise highly resistant to ferroptosis. Collectively, artemisinin compounds can sensitize cells to ferroptosis by regulating cellular iron homeostasis. Our findings can be exploited clinically to enhance the effect of future ferroptosis-inducing cancer therapies.

A survey of community search over big graphs

Abstract: With the rapid development of information technologies, various big graphs are prevalent in many real applications (e.g., social media and knowledge bases). An important component of these graphs is the network community. Essentially, a community is a group of vertices which are densely connected internally. Community retrieval can be used in many real applications, such as event organization, friend recommendation, and so on. Consequently, how to efficiently find high-quality communities from big graphs is an important research topic in the era of big data. Recently, a large group of research works, called community search, have been proposed. They aim to provide efficient solutions for searching high-quality communities from large networks in real time. Nevertheless, these works focus on different types of graphs and formulate communities in different manners, and thus, it is desirable to have a comprehensive review of these works. In this survey, we conduct a thorough review of existing community search works. Moreover, we analyze and compare the quality of communities under their models, and the performance of different solutions. Furthermore, we point out new research directions. This survey does not only help researchers to have better understanding of existing community search solutions, but also provides practitioners a better judgment on choosing the proper solutions.

Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018

Abstract: Land use reflects human activities on land. Urban land use is the highest level human alteration on Earth, and it is rapidly changing due to population increase and urbanization. Urban areas have widespread effects on local hydrology, climate, biodiversity, and food production. However, maps, that contain knowledge on the distribution, pattern and composition of various land use types in urban areas, are limited to city level. The mapping standard on data sources, methods, land use classification schemes varies from city to city, due to differences in financial input and skills of mapping personnel. To address various national and global environmental challenges caused by urbanization, it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping methods. This is because, only with urban land use maps produced with similar criteria, consistent environmental policies can be made, and action efforts can be compared and assessed for large scale environmental administration. However, despite of the fact that a number of urban-extent maps exist at global scales [3,4], more detailed urban land use maps do not exist at the same scale. Even at big country or regional levels such as for the United States, China and European Union, consistent land use mapping efforts are rare.

A Survey of Open-World Person Re-Identification

Abstract: Person re-identification (re-ID) has been a popular topic in computer vision and pattern recognition communities for a decade. Several important milestones such as metric-based and deeply-learned re-ID in recent years have promoted this topic. However, most existing re-ID works are designed for closed-world scenarios rather than realistic open-world settings, which limits the practical application of the re-ID technique. On one hand, the performance of the latest re-ID methods has surpassed the human-level performance on several commonly used benchmarks (e.g., Market1501 and CUHK03), which are collected from closed-world scenarios. On the other hand, open-world tasks that are less developed and more challenging have received increasing attention in the re-ID community. Therefore, this paper starts the first attempt to analyze the trends of open-world re-ID and summarizes them from both narrow and generalized perspectives. In the narrow perspective, open-world re-ID is regarded as person verification (i.e., open-set re-ID) instead of person identification, that is, the query person may not occur in the gallery set. In the generalized perspective, application-driven methods that are designed for specific applications are defined as generalized open-world re-ID. Their settings are usually close to realistic application requirements. Specifically, this survey mainly includes the following four points for open-world re-ID: 1) analyzing the discrepancies between closed- and open-world scenarios; 2) describing the developments of existing open-set re-ID works and their limitations; 3) introducing specific application-driven works from three aspects, namely, raw data, practical procedure, and efficiency; and 4) summarizing the state-of-the-art methods and future directions for open-world re-ID. This survey on open-world re-ID provides a guidance for improving the usability of re-ID technique in practical applications.

COVID-19 Pandemic Brings a Sedentary Lifestyle in Young Adults: A Cross-Sectional and Longitudinal Study.

Abstract: The coronavirus disease 2019 (COVID-19) pandemic continues to pose profound challenges to society. Its spread has been mitigated through strategies including social distancing; however, this may result in the adoption of a sedentary lifestyle. This study aimed to investigate: (1) physical activity (PA) levels, sedentary behavior (SB), and sleep in young adults during the COVID-19 epidemic, and (2) the change in these behaviors before and during the pandemic. A total of 631 young adults (38.8% males) aged between 18 and 35 participated in the cross-sectional study and completed a one-off online survey relating to general information, PA, SB, and sleep. For the longitudinal study, PA, SB, and sleep data, obtained from 70 participants before and during the COVID-19 pandemic, were analyzed. Participants engaged in low PA, high SB, and long sleep duration during the COVID-19 pandemic. Moreover, a significant decline in PA while an increase in time spent in both SB and sleep was observed during the COVID-19 outbreak. The results of this study demonstrated a sedentary lifestyle in young adults during the COVID-19 pandemic, which will assist health policymakers and practitioners in the development of population specific health education and behavior interventions during this pandemic and for other future events.

Single-Molecular White-Light Emitters and Their Potential WOLED Applications.

Abstract: White organic light-emitting diodes (WOLEDs) are superior to traditional incandescent light bulbs and compact fluorescent lamps in terms of their merits in ensuring pure white-light emission, low-energy consumption, large-area thin-film fabrication, etc. Unfortunately, WOLEDs based on multilayered or multicomponent (red, green, and blue (RGB)) emissive layers can suffer from some remarkable disadvantages, such as intricate device fabrication and voltage-dependent emission color, etc. Single molecules, which can emit white light, can be used to replace multiple emitters, leading to a simplified fabrication process, stable and reproducible WOLEDs. Recently, the performance of WOLEDs by using single molecules is catching up with that of the state-of-the-art devices fabricated by multicomponent emitters. Therefore, an increasing attention has been paid on single white-light-emitting materials for efficient WOLEDs. In this review, different mechanisms of white-light emission from a single molecule and the performance of single-molecule-based WOLEDs are collected and expounded, hoping to light up the interesting subject on single-molecule white-light-emitting materials, which have great potential as white-light emitters for illumination and lighting applications in the world.

Bi-Directional Center-Constrained Top-Ranking for Visible Thermal Person Re-Identification

Abstract: Visible thermal person re-identification (VT-REID) is a task of matching person images captured by thermal and visible cameras, which is an extremely important issue in night-time surveillance applications. Existing cross-modality recognition works mainly focus on learning sharable feature representations to handle the cross-modality discrepancies. However, apart from the cross-modality discrepancy caused by different camera spectrums, VT-REID also suffers from large cross-modality and intra-modality variations caused by different camera environments and human poses, and so on. In this paper, we propose a dual-path network with a novel bi-directional dual-constrained top-ranking (BDTR) loss to learn discriminative feature representations. It is featured in two aspects: 1) end-to-end learning without extra metric learning step and 2) the dual-constraint simultaneously handles the cross-modality and intra-modality variations to ensure the feature discriminability. Meanwhile, a bi-directional center-constrained top-ranking (eBDTR) is proposed to incorporate the previous two constraints into a single formula, which preserves the properties to handle both cross-modality and intra-modality variations. The extensive experiments on two cross-modality re-ID datasets demonstrate the superiority of the proposed method compared to the state-of-the-arts.

A Survey on Conversational Recommender Systems

Abstract: Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users' preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this paper, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future.

Defect-induced activity enhancement of enzyme-encapsulated metal-organic frameworks revealed in microfluidic gradient mixing synthesis

Abstract: Mimicking the cellular environment, metal-organic frameworks (MOFs) are promising for encapsulating enzymes for general applications in environments often unfavorable for native enzymes. Markedly different from previous researches based on bulk solution synthesis, here, we report the synthesis of enzyme-embedded MOFs in a microfluidic laminar flow. The continuously changed concentrations of MOF precursors in the gradient mixing on-chip resulted in structural defects in products. This defect-generating phenomenon enables multimodal pore size distribution in MOFs and therefore allows improved access of substrates to encapsulated enzymes while maintaining the protection to the enzymes. Thus, the as-produced enzyme-MOF composites showed much higher (~one order of magnitude) biological activity than those from conventional bulk solution synthesis. This work suggests that while microfluidic flow synthesis is currently underexplored, it is a promising strategy in producing highly active enzyme-MOF composites.

One app to trace them all? Examining app specifications for mass acceptance of contact-tracing apps

Abstract: The current COVID-19 crisis has seen governments worldwide mobilising to develop and implement contact-tracing apps as an integral part of their lockdown exit strategies. The challenge facing polic...

Optimally estimating the sample standard deviation from the five-number summary.

Abstract: When reporting the results of clinical studies, some researchers may choose the five-number summary (including the sample median, the first and third quartiles, and the minimum and maximum values) rather than the sample mean and standard deviation (SD), particularly for skewed data For these studies, when included in a meta-analysis, it is often desired to convert the five-number summary back to the sample mean and SD For this purpose, several methods have been proposed in the recent literature and they are increasingly used nowadays In this article, we propose to further advance the literature by developing a smoothly weighted estimator for the sample SD that fully utilizes the sample size information For ease of implementation, we also derive an approximation formula for the optimal weight, as well as a shortcut formula for the sample SD Numerical results show that our new estimator provides a more accurate estimate for normal data and also performs favorably for non-normal data Together with the optimal sample mean estimator in Luo et al, our new methods have dramatically improved the existing methods for data transformation, and they are capable to serve as "rules of thumb" in meta-analysis for studies reported with the five-number summary Finally for practical use, an Excel spreadsheet and an online calculator are also provided for implementing our optimal estimators

Practising ethically during COVID-19: Social work challenges and responses

Abstract: This article draws on findings of an international study of social workers’ ethical challenges during COVID-19, based on 607 responses to a qualitative survey. Ethical challenges included the following: maintaining trust, privacy, dignity and service user autonomy in remote relationships; allocating limited resources; balancing rights and needs of different parties; deciding whether to break or bend policies in the interests of service users; and handling emotions and ensuring care of self and colleagues. The article considers regional contrasts, the ‘ethical logistics’ of complex decision-making, the impact of societal inequities, and lessons for social workers and professional practice around the globe.

Prognostic value of depression and anxiety on breast cancer recurrence and mortality: a systematic review and meta-analysis of 282,203 patients.

Abstract: Depression and anxiety are common comorbidities in breast cancer patients. Whether depression and anxiety are associated with breast cancer progression or mortality is unclear. Herein, based on a systematic literature search, 17 eligible studies involving 282,203 breast cancer patients were included. The results showed that depression was associated with cancer recurrence [1.24 (1.07, 1.43)], all-cause mortality [1.30 (1.23, 1.36)], and cancer-specific mortality [1.29 (1.11, 1.49)]. However, anxiety was associated with recurrence [1.17 (1.02, 1.34)] and all-cause mortality [1.13 (1.07, 1.19)] but not with cancer-specific mortality [1.05 (0.82, 1.35)]. Comorbidity of depression and anxiety is associated with all-cause mortality [1.34 (1.24, 1.45)] and cancer-specific mortality [1.45 (1.11, 1.90)]. Subgroup analyses demonstrated that clinically diagnosed depression and anxiety, being female and of younger age (<60 years), and shorter follow-up duration (≤5 years) were related to a poorer prognosis. Our study highlights the critical role of depression/anxiety as an independent factor in predicting breast cancer recurrence and survival. Further research should focus on a favorable strategy that works best to improve outcomes among breast cancer patients with mental disorders.

Cross-Modality Person Re-Identification via Modality-Aware Collaborative Ensemble Learning

Abstract: Visible thermal person re-identification (VT-ReID) is a challenging cross-modality pedestrian retrieval problem due to the large intra-class variations and modality discrepancy across different cameras. Existing VT-ReID methods mainly focus on learning cross-modality sharable feature representations by handling the modality-discrepancy in feature level. However, the modality difference in classifier level has received much less attention, resulting in limited discriminability. In this paper, we propose a novel modality-aware collaborative ensemble (MACE) learning method with middle-level sharable two-stream network (MSTN) for VT-ReID, which handles the modality-discrepancy in both feature level and classifier level. In feature level, MSTN achieves much better performance than existing methods by capturing sharable discriminative middle-level features in convolutional layers. In classifier level, we introduce both modality-specific and modality-sharable identity classifiers for two modalities to handle the modality discrepancy. To utilize the complementary information among different classifiers, we propose an ensemble learning scheme to incorporate the modality sharable classifier and the modality specific classifiers. In addition, we introduce a collaborative learning strategy, which regularizes modality-specific identity predictions and the ensemble outputs. Extensive experiments on two cross-modality datasets demonstrate that the proposed method outperforms current state-of-the-art by a large margin, achieving rank-1/mAP accuracy 51.64%/50.11% on the SYSU-MM01 dataset, and 72.37%/69.09% on the RegDB dataset.

Near-infrared and visible light dual-mode organic photodetectors.

Abstract: We report a dual-mode organic photodetector (OPD) that has a trilayer visible light absorber/optical spacer/near-infrared (NIR) light absorber configuration. In the presence of NIR light, photocurrent is produced in the NIR light-absorbing layer due to the trap-assisted charge injection at the organic/cathode interface at a reverse bias. In the presence of visible light, photocurrent is produced in the visible light-absorbing layer, enabled by the trap-assisted charge injection at the anode/organic interface at a forward bias. A high responsivity of >10 A/W is obtained in both short and long wavelengths. The dual-mode OPD exhibits an NIR light response operated at a reverse bias and a visible light response operated at a forward bias, with a high specific detectivity of ~1013 Jones in both NIR and visible light ranges. A bias-switchable spectral response OPD offers an attractive option for applications in environmental pollution detection, bioimaging process, wellness, and security monitoring in two distinct bands.

A Loop-Based and AGO-Incorporated Virtual Screening Model Targeting AGO-Mediated miRNA-mRNA Interactions for Drug Discovery to Rescue Bone Phenotype in Genetically Modified Mice.

Abstract: Several virtual screening models are proposed to screen small molecules only targeting primary miRNAs without selectivity. Few attempts have been made to develop virtual screening strategies for discovering small molecules targeting mature miRNAs. Mature miRNAs and their specific target mRNA can form unique functional loops during argonaute (AGO)-mediated miRNA-mRNA interactions, which may serve as potential targets for small-molecule drug discovery. Thus, a loop-based and AGO-incorporated virtual screening model is constructed for targeting the loops. The previously published studies have found that miR-214 can target ATF4 to inhibit osteoblastic bone formation, whereas miR-214 can target TRAF3 to promote osteoclast activity. By using the virtual model, the top ten candidate small molecules targeting miR-214-ATF4 mRNA interactions and top ten candidate small molecules targeting miR-214-TRAF3 mRNA interactions are selected, respectively. Based on both in vitro and in vivo data, one small molecule can target miR-214-ATF4 mRNA to promote ATF4 protein expression and enhance osteogenic potential, whereas one small molecule can target miR-214-TRAF3 mRNA to promote TRAF3 protein expression and inhibit osteoclast activity. These data indicate that the loop-based and AGO-incorporated virtual screening model can help to obtain small molecules specifically targeting miRNA-mRNA interactions to rescue bone phenotype in genetically modified mice.

FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC

Abstract: Promising federated learning coupled with Mobile Edge Computing (MEC) is considered as one of the most promising solutions to the AI-driven service provision. Plenty of studies focus on federated learning from the performance and security aspects, but they neglect the incentive mechanism. In MEC, edge nodes would not like to voluntarily participate in learning, and they differ in the provision of multi-dimensional resources, both of which might deteriorate the performance of federated learning. Also, lightweight schemes appeal to edge nodes in MEC. These features require the incentive mechanism to be well designed for MEC. In this paper, we present an incentive mechanism FMore with multi-dimensional procurement auction of K winners. Our proposal FMore not only is lightweight and incentive compatible, but also encourages more high-quality edge nodes with low cost to participate in learning and eventually improve the performance of federated learning. We also present theoretical results of Nash equilibrium strategy to edge nodes and employ the expected utility theory to provide guidance to the aggregator. Both extensive simulations and real-world experiments demonstrate that the proposed scheme can effectively reduce the training rounds and drastically improve the model accuracy for challenging AI tasks.

Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning

Abstract: The \textit{transition matrix}, denoting the transition relationship from clean labels to noisy labels, is essential to build \textit{statistically consistent} classifiers in label-noise learning. Existing methods for estimating the transition matrix rely heavily on estimating the noisy class posterior. However, the estimation error for \textit{noisy class posterior} could be large due to the randomness of label noise. The estimation error would lead the transition matrix to be poorly estimated. Therefore, in this paper, we aim to solve this problem by exploiting the divide-and-conquer paradigm. Specifically, we introduce an \textit{intermediate class} to avoid directly estimating the noisy class posterior. By this intermediate class, the original transition matrix can then be factorized into the product of two easy-to-estimate transition matrices. We term the proposed method the \textit{dual $T$-estimator}. Both theoretical analyses and empirical results illustrate the effectiveness of the dual $T$-estimator for estimating transition matrices, leading to better classification performances.

Decision-making techniques in supplier selection: Recent accomplishments and what lies ahead

Abstract: Supplier selection (SS) is considered a sophisticated, application-oriented, decision-making (DM) problem and has received considerable attention. In the past two decades, DM theories and techniques continue to be incorporated into and contribute to the development of SS applications. Maintaining the pace of the rapid transitions in this field, this paper systematically reviews the relevant articles published between 2013 and 2018. Articles that orient various DM techniques are selected and analyzed under a well-established framework. State-of-the-art developments in the adoption of DM techniques are summarized in a SS process. We pay particular attention to promising directions that can dominate future research in this field. This paper further extends the history of several interacting fields, including big data and economic theories, toward methodological rather than application dimensions. The potential of such fields for SS is discussed from an interdisciplinary perspective.